python - manipulating multiindex columns in Pandas -

i have simple multiindex dataframe in pandas. i'm trying add additional subcolumns, i'm being warned off

settingwithcopywarning:  value trying set on copy of slice dataframe. try using .loc[row_indexer,col_indexer] = value instead 

i cannot manage right indexing incantation make work.

attaching code fragment has simple, non-hierarchical example of sorts of columns want add. have hierchical example demonstrate how can add new top-level colummns, cannot manipulate individual sub-columns

import pandas pd import numpy np   #simple example works: add 2 columns non-hierarchical frame sdf = pd.dataframe(np.random.randn(6,4),columns=list('abcd')) sdf['e'] = 7 sdf['f'] = sdf['a'].diff(-1)   #hierarchical example df = pd.dataframe({('co1', 'price'): {0: 1, 1: 2, 2:12, 3: 14, 4: 15},\ ('co1', 'size'): {0: 1, 1: 5, 2: 9, 3: 13, 4: 17},\ ('co2', 'price'): {0: 2, 1: 6, 2: 10, 3: 14, 4: 18},\ ('co2', 'size'): {0: 3, 1: 7, 2: 11, 3: 15, 4: 19}})  df.index.names = ['run'] df.columns.names = ['security', 'characteristic']  #i can add new top level column df['newtoplevel?'] = "yes"   #i cannot manipulate values of existing sub-level columns """a value trying set on copy of slice dataframe. try using .loc[row_indexer,col_indexer] = value instead"""  df['co1']['size'] = "gross" df['co1']['price'] = df['co1']['price']*2   #i cannot add new sub-level column df['co1']['new_sub_col'] = "fails" 

i seem missing fundamental understanding of issue, frustrating i've read pretty closely o'reilly "python data analysis" book written pandas author! ugh.

to avoid warning/error use loc , these in 1 assignment:

in [11]: df.loc[:, ('co1', 'size')] = "gross"  in [12]: df.loc[:, ('co1', 'price')] *= 2  in [13]: df.loc[:, ('co1', 'new_sub_col')] = "fails"  # not anymore  in [14]: df out[14]: security         co1          co2      newtoplevel?         co1 characteristic price   size price size              new_sub_col run 0                  2  gross     2    3          yes       fails 1                  4  gross     6    7          yes       fails 2                 24  gross    10   11          yes       fails 3                 28  gross    14   15          yes       fails 4                 30  gross    18   19          yes       fails